Reddit Machine Learning Engineer Interview Guide

Reddit MLEs don't just build models. They own the production systems that decide what 50+ million daily active users see in their feeds, which ads get shown alongside that content, and which posts get flagged before they cause harm. Candidates who prep for a generic "big tech MLE" loop and ignore how Reddit's community structure shapes every ranking decision tend to underperform in the system design rounds.

Reddit Machine Learning Engineer Role

Reddit MLEs own the full lifecycle of models powering the home feed, subreddit discovery, ads targeting, and content safety. You're not handing prototypes to a platform team. Success after year one looks like shipping multiple model iterations to production, running A/B tests that account for Reddit's community-level interference effects, and building enough product context to reason about how a ranking tweak that boosts engagement in r/gaming might suppress visibility in r/AskHistorians.

A Typical Week

The split that surprises most candidates is how little time goes to pure modeling versus the operational work surrounding it. You'll spend a Wednesday morning reviewing A/B results with an Ads data science partner, then Thursday afternoon reviewing a Trust & Safety team's NSFW classifier threshold change in PyTorch, then Friday morning packaging a model artifact for canary rollout on Kubernetes. The iteration loop (ship a ranking change, monitor it across subreddits with very different traffic patterns, decide whether to roll back) is the actual job.

Projects & Impact Areas

Feed ranking is the gravitational center: Reddit's home feed, "Best" sort, and subreddit recommendations all run on ML models that must handle brutal cold-start problems when new communities spin up or lurkers with zero engagement history appear. That feed engagement is what makes Reddit's advertising business work, where contextual and behavioral targeting operates in a pseudonymous environment with far thinner identity signals than platforms with rich identity graphs. Content safety rounds out the picture, with models detecting spam, vote manipulation, and policy-violating content across text, images, and video.

Skills & What's Expected

Production engineering chops are what separates candidates who clear the bar from those who don't. The skill profile rates software engineering at expert level, and that means owning feature pipelines in Python or Scala, debugging flaky Spark jobs in Airflow, and configuring Kubernetes canary deployments. Business acumen sits at medium, which doesn't mean you can skip it. Interviewers will probe whether you understand how feed engagement translates to ad impressions, so you need a working mental model of Reddit's revenue mechanics even if you're not setting OKRs.

Levels & Career Growth

The jump from IC4 (Senior) to IC5 (Staff) is where careers stall, and it's almost always about scope rather than technical skill. IC4 engineers own a model and its iteration cycle, while Staff engineers define the technical direction for an entire ML surface like feed ranking, including serving architecture, experimentation framework, and cross-team alignment. Reddit's relatively small engineering org means senior MLEs get outsized visibility and can influence platform-wide ML infrastructure decisions earlier in their career than at much larger companies.

Work Culture

Reddit operates as remote-first, though the SF office draws Bay Area folks on team sync days. The engineering culture favors ownership and shipping speed over heavyweight review processes, which means you'll move fast but need to be self-directed about career development and mentorship. Reddit's published values emphasize "Remember the Human," and in practice MLEs are expected to consider how ranking changes affect smaller communities rather than just optimizing aggregate engagement metrics.

Reddit Machine Learning Engineer Compensation

No vesting schedule, grant size, or refresh grant details are publicly confirmed for Reddit MLE roles. Ask your recruiter point-blank whether RSUs follow a 4-year vest with a 1-year cliff or use any backloading, because that single detail reshapes your actual Year 1 take-home more than anything else in the offer letter. Push equally hard on annual refresh grants: without them, your effective comp erodes each year as the initial grant vests out.

Look at the spread between tc_min and tc_max at IC4 (roughly $248K to $701K). That range tells you there's room to move, and the offer_negotiation_notes confirm equity is where most of that flex lives. Bring a written competing offer, ask for the full compensation band for your level, and anchor your RSU ask near the top of it. A sign-on bonus is also worth requesting if you're walking away from unvested equity elsewhere.

Reddit Machine Learning Engineer Interview Process

The most common rejection pattern, from what candidates report, is strong modeling paired with weak experimentation design. Reddit's subreddit structure makes A/B testing genuinely tricky: users participate in overlapping communities, so a ranking change in r/nba can ripple into r/sports through cross-posted content and shared users. Candidates who can't reason about interference effects, or who propose guardrails that stop at CTR without mentioning community health signals like report rates and content diversity, tend to underperform in both the Product Sense & Metrics and ML System Design rounds.

Don't sleep on the Product Sense & Metrics round. Many MLEs barely prep for it, assuming the technical rounds carry all the weight. But Reddit's product is 100K+ communities with wildly different norms, and the round specifically probes whether you'll blindly optimize engagement at the expense of smaller subreddits. Prepare for it with the same rigor you'd give system design. Practice framing metric tradeoffs and experiment designs at datainterview.com/questions, especially scenarios where engagement and content quality pull in opposite directions.

Reddit Machine Learning Engineer Interview Questions

1from collections import deque
2from typing import List
3
4
5def sliding_window_max(scores: List[int], k: int) -> List[int]:
6    """Return the maximum score for each contiguous window of size k.
7
8    Time: O(n)
9    Space: O(k)
10
11    Args:
12        scores: List of integer scores.
13        k: Window size.
14
15    Returns:
16        List of window maxima, length max(0, n-k+1).
17    """
18    n = len(scores)
19    if k <= 0:
20        raise ValueError("k must be positive")
21    if k > n:
22        return []
23
24    # dq stores indices, and scores[dq] is in strictly decreasing order.
25    # For ties, drop the older index so the newer one survives longer.
26    dq = deque()
27    out: List[int] = []
28
29    for i, x in enumerate(scores):
30        # Remove indices that are out of the current window.
31        window_start = i - k + 1
32        while dq and dq[0] < window_start:
33            dq.popleft()
34
35        # Maintain decreasing order, drop <= to keep newest on ties.
36        while dq and scores[dq[-1]] <= x:
37            dq.pop()
38        dq.append(i)
39
40        # Start outputting once the first full window is formed.
41        if i >= k - 1:
42            out.append(scores[dq[0]])
43
44    return out
45
46
47if __name__ == "__main__":
48    assert sliding_window_max([1, 3, -1, -3, 5, 3, 6, 7], 3) == [3, 3, 5, 5, 6, 7]
49    assert sliding_window_max([2, 2, 2], 2) == [2, 2]
50    assert sliding_window_max([9], 1) == [9]
51

1from typing import Optional, Dict
2
3
4class _Node:
5    __slots__ = ("val", "prev", "next")
6
7    def __init__(self, val: int):
8        self.val = val
9        self.prev: Optional["_Node"] = None
10        self.next: Optional["_Node"] = None
11
12
13class FirstUniqueStream:
14    """Maintain the first value that has appeared exactly once in a stream.
15
16    Operations:
17      - add(x): O(1) amortized
18      - first_unique(): O(1)
19
20    Implementation uses:
21      - count map
22      - node map for values currently unique
23      - doubly linked list of unique values in arrival order
24    """
25
26    def __init__(self):
27        self._count: Dict[int, int] = {}
28        self._node: Dict[int, _Node] = {}
29
30        # Sentinel head/tail to simplify removals.
31        self._head = _Node(-1)
32        self._tail = _Node(-1)
33        self._head.next = self._tail
34        self._tail.prev = self._head
35
36    def _append(self, node: _Node) -> None:
37        last = self._tail.prev
38        assert last is not None
39        last.next = node
40        node.prev = last
41        node.next = self._tail
42        self._tail.prev = node
43
44    def _remove(self, node: _Node) -> None:
45        p, n = node.prev, node.next
46        assert p is not None and n is not None
47        p.next = n
48        n.prev = p
49        node.prev = None
50        node.next = None
51
52    def add(self, post_id: int) -> None:
53        c = self._count.get(post_id, 0) + 1
54        self._count[post_id] = c
55
56        if c == 1:
57            node = _Node(post_id)
58            self._node[post_id] = node
59            self._append(node)
60        elif c == 2:
61            node = self._node.pop(post_id, None)
62            if node is not None:
63                self._remove(node)
64        # If c > 2, nothing to do.
65
66    def first_unique(self) -> Optional[int]:
67        first = self._head.next
68        if first is None or first is self._tail:
69            return None
70        return first.val
71
72
73if __name__ == "__main__":
74    s = FirstUniqueStream()
75    for x in [5, 7, 5, 9, 7, 10]:
76        s.add(x)
77    assert s.first_unique() == 9
78    s.add(9)
79    assert s.first_unique() == 10
80    s.add(10)
81    assert s.first_unique() is None
82

1WITH impressions AS (
2  SELECT
3    impression_id,
4    model_version,
5    DATE_TRUNC('day', ts) AS day
6  FROM feed_impression
7  WHERE surface = 'home'
8    AND ts >= CURRENT_DATE - INTERVAL '7 days'
9),
10clicks_dedup AS (
11  -- Deduplicate to at most one click per impression.
12  SELECT
13    impression_id,
14    1 AS clicked
15  FROM (
16    SELECT
17      impression_id,
18      ROW_NUMBER() OVER (PARTITION BY impression_id ORDER BY ts ASC) AS rn
19    FROM feed_click
20    WHERE ts >= CURRENT_DATE - INTERVAL '7 days'
21  ) c
22  WHERE rn = 1
23)
24SELECT
25  i.day,
26  i.model_version,
27  COUNT(*) AS impressions,
28  SUM(COALESCE(cd.clicked, 0)) AS clicks,
29  1.0 * SUM(COALESCE(cd.clicked, 0)) / NULLIF(COUNT(*), 0) AS ctr
30FROM impressions i
31LEFT JOIN clicks_dedup cd
32  ON cd.impression_id = i.impression_id
33GROUP BY 1, 2
34ORDER BY 1 DESC, 2;

1WITH base AS (
2  SELECT
3    request_id,
4    user_id,
5    post_id,
6    rank_position,
7    model_version,
8    DATE_TRUNC('day', ts) AS day
9  FROM impression_log
10  WHERE ts >= CURRENT_DATE - INTERVAL '14 days'
11),
12-- Binary relevance per (request_id, post_id): did it receive at least one click event.
13relevance AS (
14  SELECT
15    request_id,
16    post_id,
17    1 AS rel
18  FROM (
19    SELECT
20      request_id,
21      post_id,
22      ROW_NUMBER() OVER (PARTITION BY request_id, post_id ORDER BY ts ASC) AS rn
23    FROM engagement_log
24    WHERE event_type = 'click'
25      AND ts >= CURRENT_DATE - INTERVAL '14 days'
26  ) e
27  WHERE rn = 1
28),
29scored AS (
30  SELECT
31    b.day,
32    b.model_version,
33    b.request_id,
34    b.rank_position,
35    COALESCE(r.rel, 0) AS rel
36  FROM base b
37  LEFT JOIN relevance r
38    ON r.request_id = b.request_id
39   AND r.post_id = b.post_id
40  WHERE b.rank_position BETWEEN 1 AND 10
41),
42request_stats AS (
43  SELECT
44    day,
45    model_version,
46    request_id,
47    -- DCG with binary relevance: sum rel / log2(rank+1)
48    SUM(
49      rel / (LN(rank_position + 1) / LN(2))
50    ) AS dcg,
51    -- Count of relevant items in the evaluated top 10
52    SUM(rel) AS rel_in_top10,
53    COUNT(*) AS impressions_in_top10
54  FROM scored
55  GROUP BY 1, 2, 3
56),
57request_idcg AS (
58  SELECT
59    day,
60    model_version,
61    request_id,
62    dcg,
63    rel_in_top10,
64    impressions_in_top10,
65    -- IDCG for binary relevance is sum_{i=1..k} 1/log2(i+1), where k = rel_in_top10.
66    (
67      SELECT
68        COALESCE(SUM(1.0 / (LN(pos + 1) / LN(2))), 0.0)
69      FROM (
70        SELECT
71          GENERATE_SERIES(1, CAST(rel_in_top10 AS INT)) AS pos
72      ) s
73    ) AS idcg
74  FROM request_stats
75)
76SELECT
77  day,
78  model_version,
79  COUNT(*) AS requests,
80  AVG(
81    CASE
82      WHEN idcg = 0 THEN 0.0
83      ELSE dcg / idcg
84    END
85  ) AS ndcg_at_10,
86  AVG(impressions_in_top10) AS avg_impressions_evaluated
87FROM request_idcg
88GROUP BY 1, 2
89ORDER BY 1 DESC, 2;

The distribution is lopsided toward system design and modeling, and at Reddit those two areas bleed into each other. You can't design a Home feed serving path without explaining how post churn (new content every few minutes across wildly different subreddits) shapes your negative sampling and retraining cadence. The most common prep mistake is treating coding and SQL as equal priorities to experimentation, when the experimentation round asks you to reason about A/B test interference caused by Reddit's overlapping community structure, something most engineers from non-social-graph companies have never practiced.

Practice Reddit-style ranking and recommendation questions at datainterview.com/questions.

How to Prepare for Reddit Machine Learning Engineer Interviews

Reddit pulled in $2.2B in full-year 2025 revenue, up roughly 70% year-over-year, with advertising as the primary revenue driver. But the company's bets are spreading: an AI-powered shopping search feature aims to turn community product discussions into a commerce funnel, and content safety and integrity systems remain a constant investment area for a platform built on user-generated content.

For day-to-day MLE work, that means you could be improving feed ranking one quarter and building retrieval models for shopping the next, all while the trust and safety org leans on your team for content understanding models. Read the 2024 annual report before your loop so you can speak fluently about where ML fits across these surfaces.

Most candidates blow their "why Reddit" answer by talking about how much they love browsing the site. What actually lands: naming the ML constraints that make Reddit's problems distinct. Pseudonymous users give you far weaker identity signals than a Meta or Google identity graph. New communities spin up constantly, creating cold-start problems that don't exist on platforms with stable content taxonomies. Frame your motivation around those technical puzzles, not your favorite subreddits.

Try a Real Interview Question

1from typing import Dict, Iterable, List, Hashable
2
3
4def ndcg_at_k(predicted: List[Hashable], relevance: Dict[Hashable, float], k: int) -> float:
5    """Compute NDCG@k for a ranking.
6
7    Args:
8        predicted: Ranked list of item ids, highest rank first.
9        relevance: Mapping from item id to graded relevance score (non-negative).
10        k: Rank cutoff.
11
12    Returns:
13        NDCG@k as a float in [0, 1]. If IDCG@k is 0, return 0.0.
14    """
15    pass
16

1from typing import Dict, List, Hashable
2import math
3
4
5def ndcg_at_k(predicted: List[Hashable], relevance: Dict[Hashable, float], k: int) -> float:
6    """Compute NDCG@k for a ranking.
7
8    Args:
9        predicted: Ranked list of item ids, highest rank first.
10        relevance: Mapping from item id to graded relevance score (non-negative).
11        k: Rank cutoff.
12
13    Returns:
14        NDCG@k as a float in [0, 1]. If IDCG@k is 0, return 0.0.
15
16    Notes:
17        Uses DCG@k = sum_{j=1..k} (2^{rel_j} - 1) / log2(j + 1).
18        Items not present in relevance are treated as rel = 0.
19    """
20    if k <= 0:
21        return 0.0
22
23    def gain(rel: float) -> float:
24        if rel <= 0.0:
25            return 0.0
26        return (2.0 ** rel) - 1.0
27
28    def discount(rank_index_zero_based: int) -> float:
29        j = rank_index_zero_based + 1
30        return math.log2(j + 1)
31
32    k_eff = min(k, len(predicted))
33    dcg = 0.0
34    for idx in range(k_eff):
35        item_id = predicted[idx]
36        rel = float(relevance.get(item_id, 0.0))
37        if rel < 0.0:
38            raise ValueError("relevance scores must be non-negative")
39        dcg += gain(rel) / discount(idx)
40
41    # Ideal DCG: sort the relevance scores of the same set of candidate items.
42    # Here we use all provided relevance labels, which is common when labels represent
43    # the judged set; if the judged set is smaller than k, pad with zeros implicitly.
44    rels_sorted = sorted((float(r) for r in relevance.values()), reverse=True)
45    idcg = 0.0
46    for idx in range(min(k, len(rels_sorted))):
47        rel = rels_sorted[idx]
48        if rel < 0.0:
49            raise ValueError("relevance scores must be non-negative")
50        idcg += gain(rel) / discount(idx)
51
52    if idcg == 0.0:
53        return 0.0
54    return dcg / idcg
55

Reddit's coding round is a gate, not a differentiator, so the problems tend to test clean implementation and edge-case handling rather than obscure algorithmic tricks. Where it gets Reddit-specific: from what candidates report, expect scenarios that touch string processing or graph traversal patterns reminiscent of comment trees and community relationships. Keep your skills warm with regular reps on datainterview.com/coding.

Test Your Readiness

After this quiz, practice ML system design and ranking problems at datainterview.com/questions, focusing on scenarios where user intent varies across distinct community contexts.